Mitigation of nitrous oxide emissions in grazing systems through nitrification inhibitors: a meta-analysis

Grasslands are the largest contributor of nitrous oxide (N2O) emissions in the agriculture sector due to livestock excreta and nitrogen fertilizers applied to the soil. Nitrification inhibitors (NIs) added to N input have reduced N2O emissions, but can show a range of efficiencies depending on climate, soil, and management conditions. A meta-analysis study was conducted to investigate the factors that influence the efficiency of NIs added to fertilizer and excreta in reducing N2O emissions, focused on grazing systems. Data from peer-reviewed studies comprising 2164 N2O emission factors (EFs) of N inputs with and without NIs addition were compared. The N2O EFs varied according to N source (0.0001-8.25%). Overall, NIs reduced the N2O EF from N addition by 56.6% (51.1-61.5%), with no difference between NI types (Dicyandiamide-DCD; 3,4-Dimethylpyrazole phosphate-DMPP; and Nitrapyrin) or N source (urine, dung, slurry, and fertilizer). The NIs were more efficient in situations of high N2O emissions compared with low; the reduction was 66.0% when EF > 1.5% of N applied compared with 51.9% when EF 10 kg ha(-1). NIs were less efficient in urine with lower N content (<= 7 g kg(-1)). NI efficiency was negatively correlated with soil bulk density, and positively correlated with soil moisture and temperature. Better understanding and management of NIs can optimize N2O mitigation in grazing systems, e.g., by mapping N2O risk and applying NI at variable rate, contributing to improved livestock sustainability

Data standardization of plant-pollinator interactions

Background Animal pollination is an important ecosystem function and service, ensuring both the integrity of natural systems and human well-being. Although many knowledge shortfalls remain, some high-quality data sets on biological interactions are now available. The development and adoption of standards for biodiversity data and metadata has promoted great advances in biological data sharing and aggregation, supporting large-scale studies and science-based public policies. However, these standards are currently not suitable to fully support interaction data sharing. Results Here we present a vocabulary of terms and a data model for sharing plant–pollinator interactions data based on the Darwin Core standard. The vocabulary introduces 48 new terms targeting several aspects of plant–pollinator interactions and can be used to capture information from different approaches and scales. Additionally, we provide solutions for data serialization using RDF, XML, and DwC-Archives and recommendations of existing controlled vocabularies for some of the terms. Our contribution supports open access to standardized data on plant–pollinator interactions. Conclusions The adoption of the vocabulary would facilitate data sharing to support studies ranging from the spatial and temporal distribution of interactions to the taxonomic, phenological, functional, and phylogenetic aspects of plant–pollinator interactions. We expect to fill data and knowledge gaps, thus further enabling scientific research on the ecology and evolution of plant–pollinator communities, biodiversity conservation, ecosystem services, and the development of public policies. The proposed data model is flexible and can be adapted for sharing other types of interactions data by developing discipline-specific vocabularies of terms

Data standardization of plant-pollinator interactions

Background: Animal pollination is an important ecosystem function and service, ensuring both the integrity of natural systems and human well-being. Although many knowledge shortfalls remain, some high-quality data sets on biological interactions are now available. The development and adoption of standards for biodiversity data and metadata has promoted great advances in biological data sharing and aggregation, supporting large-scale studies and science-based public policies. However, these standards are currently not suitable to fully support interaction data sharing. Results: Here we present a vocabulary of terms and a data model for sharing plant–pollinator interactions data based on the Darwin Core standard. The vocabulary introduces 48 new terms targeting several aspects of plant–pollinator interactions and can be used to capture information from different approaches and scales. Additionally, we provide solutions for data serialization using RDF, XML, and DwC-Archives and recommendations of existing controlled vocabularies for some of the terms. Our contribution supports open access to standardized data on plant–pollinator interactions. Conclusions: The adoption of the vocabulary would facilitate data sharing to support studies ranging from the spatial and temporal distribution of interactions to the taxonomic, phenological, functional, and phylogenetic aspects of plant–pollinator interactions. We expect to fill data and knowledge gaps, thus further enabling scientific research on the ecology and evolution of plant–pollinator communities, biodiversity conservation, ecosystem services, and the development of public policies. The proposed data model is flexible and can be adapted for sharing other types of interactions data by developing discipline-specific vocabularies of terms.Fil: Salim, José A. Universidade de Sao Paulo; BrasilFil: Saraiva, Antonio M.. Universidade de Sao Paulo; BrasilFil: Zermoglio, Paula Florencia. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - Patagonia Norte. Instituto de Investigaciones En Recursos Naturales, Agroecologia y Desarrollo Rural. - Universidad Nacional de Rio Negro. Instituto de Investigaciones En Recursos Naturales, Agroecologia y Desarrollo Rural.; ArgentinaFil: Agostini, Kayna. Universidade Federal do São Carlos; BrasilFil: Wolowski, Marina. Universidade Federal de Alfenas; BrasilFil: Drucker, Debora P.. Empresa Brasileira de Pesquisa Agropecuaria (embrapa);Fil: Soares, Filipi M.. Universidade de Sao Paulo; BrasilFil: Bergamo, Pedro J.. Jardim Botânico do Rio de Janeiro; BrasilFil: Varassin, Isabela G.. Universidade Federal do Paraná; BrasilFil: Freitas, Leandro. Jardim Botânico do Rio de Janeiro; BrasilFil: Maués, Márcia M.. Empresa Brasileira de Pesquisa Agropecuaria (embrapa);Fil: Rech, Andre R.. Universidade Federal dos Vales do Jequitinhonha e Mucuri; BrasilFil: Veiga, Allan K.. Universidade de Sao Paulo; BrasilFil: Acosta, Andre L.. Instituto Tecnológico Vale; BrasilFil: Araujo, Andréa C. Universidade Federal do Mato Grosso do Sul; BrasilFil: Nogueira, Anselmo. Universidad Federal do Abc; BrasilFil: Blochtein, Betina. Pontificia Universidade Católica do Rio Grande do Sul; BrasilFil: Freitas, Breno M.. Universidade Estadual do Ceará; BrasilFil: Albertini, Bruno C.. Universidade de Sao Paulo; BrasilFil: Maia Silva, Camila. Universidade Federal Rural Do Semi Arido; BrasilFil: Nunes, Carlos E. P.. University of Stirling; BrasilFil: Pires, Carmen S. S.. Empresa Brasileira de Pesquisa Agropecuaria (embrapa);Fil: Dos Santos, Charles F.. Pontificia Universidade Católica do Rio Grande do Sul; BrasilFil: Queiroz, Elisa P.. Universidade de Sao Paulo; BrasilFil: Cartolano, Etienne A.. Universidade de Sao Paulo; BrasilFil: de Oliveira, Favízia F. Universidade Federal da Bahia; BrasilFil: Amorim, Felipe W.. Universidade Estadual Paulista Julio de Mesquita Filho; BrasilFil: Fontúrbel, Francisco E.. Pontificia Universidad Católica de Valparaíso; ChileFil: da Silva, Gleycon V.. Ministério da Ciência, Tecnologia, Inovações. Instituto Nacional de Pesquisas da Amazônia; BrasilFil: Consolaro, Hélder. Universidade Federal de Catalão; Brasi

Data standardization of plant–pollinator interactions

Background: Animal pollination is an important ecosystem function and service, ensuring both the integrity of natural systems and human well-being. Although many knowledge shortfalls remain, some high-quality data sets on biological interactions are now available. The development and adoption of standards for biodiversity data and metadata has promoted great advances in biological data sharing and aggregation, supporting large-scale studies and science-based public policies. However, these standards are currently not suitable to fully support interaction data sharing. Results: Here we present a vocabulary of terms and a data model for sharing plant–pollinator interactions data based on the Darwin Core standard. The vocabulary introduces 48 new terms targeting several aspects of plant–pollinator interactions and can be used to capture information from different approaches and scales. Additionally, we provide solutions for data serialization using RDF, XML, and DwC-Archives and recommendations of existing controlled vocabularies for some of the terms. Our contribution supports open access to standardized data on plant–pollinator interactions. Conclusions: The adoption of the vocabulary would facilitate data sharing to support studies ranging from the spatial and temporal distribution of interactions to the taxonomic, phenological, functional, and phylogenetic aspects of plant–pollinator interactions. We expect to fill data and knowledge gaps, thus further enabling scientific research on the ecology and evolution of plant–pollinator communities, biodiversity conservation, ecosystem services, and the development of public policies. The proposed data model is flexible and can be adapted for sharing other types of interactions data by developing discipline-specific vocabularies of termsinfo:eu-repo/semantics/publishedVersio

Psychopharmacotherapy of panic disorder: 8-week randomized trial with clonazepam and paroxetine

The objective of the present randomized, open-label, naturalistic 8-week study was to compare the efficacy and safety of treat- ment with clonazepam (N = 63) and paroxetine (N = 57) in patients with panic disorder with or without agoraphobia. Efficacy assessment included number of panic attacks and clinician ratings of the global severity of panic disorders with the clinical global impression (CGI) improvement (CGI-I) and CGI severity (CGI-S) scales. Most patients were females (69.8 and 68.4% in the clonazepam and paroxetine groups, respectively) and age (mean ± SD) was 35.9 ± 9.6 years for the clonazepam group and 33.7 ± 8.8 years for the paroxetine group. Treatment with clonazepam versus paroxetine resulted in fewer weekly panic attacks at week 4 (0.1 vs 0.5, respectively; P < 0.01), and greater clinical improvements at week 8 (CGI-I: 1.6 vs 2.9; P = 0.04). Anxiety severity was significantly reduced with clonazepam versus paroxetine at weeks 1 and 2, with no difference in panic disorder severity. Patients treated with clonazepam had fewer adverse events than patients treated with paroxetine (73 vs 95%; P = 0.001). The most common adverse events were drowsiness/fatigue (57%), memory/concentration difficulties (24%), and sexual dysfunction (11%) in the clonazepam group and drowsiness/fatigue (81%), sexual dysfunction (70%), and nausea/vomiting (61%) in the paroxetine group. This naturalistic study confirms the efficacy and tolerability of clonazepam and paroxetine in the acute treatment of patients with panic disorder

Effects of hospital facilities on patient outcomes after cancer surgery: an international, prospective, observational study

Background Early death after cancer surgery is higher in low-income and middle-income countries (LMICs) compared with in high-income countries, yet the impact of facility characteristics on early postoperative outcomes is unknown. The aim of this study was to examine the association between hospital infrastructure, resource availability, and processes on early outcomes after cancer surgery worldwide.Methods A multimethods analysis was performed as part of the GlobalSurg 3 study-a multicentre, international, prospective cohort study of patients who had surgery for breast, colorectal, or gastric cancer. The primary outcomes were 30-day mortality and 30-day major complication rates. Potentially beneficial hospital facilities were identified by variable selection to select those associated with 30-day mortality. Adjusted outcomes were determined using generalised estimating equations to account for patient characteristics and country-income group, with population stratification by hospital.Findings Between April 1, 2018, and April 23, 2019, facility-level data were collected for 9685 patients across 238 hospitals in 66 countries (91 hospitals in 20 high-income countries; 57 hospitals in 19 upper-middle-income countries; and 90 hospitals in 27 low-income to lower-middle-income countries). The availability of five hospital facilities was inversely associated with mortality: ultrasound, CT scanner, critical care unit, opioid analgesia, and oncologist. After adjustment for case-mix and country income group, hospitals with three or fewer of these facilities (62 hospitals, 1294 patients) had higher mortality compared with those with four or five (adjusted odds ratio [OR] 3.85 [95% CI 2.58-5.75]; p&lt;0.0001), with excess mortality predominantly explained by a limited capacity to rescue following the development of major complications (63.0% vs 82.7%; OR 0.35 [0.23-0.53]; p&lt;0.0001). Across LMICs, improvements in hospital facilities would prevent one to three deaths for every 100 patients undergoing surgery for cancer.Interpretation Hospitals with higher levels of infrastructure and resources have better outcomes after cancer surgery, independent of country income. Without urgent strengthening of hospital infrastructure and resources, the reductions in cancer-associated mortality associated with improved access will not be realised

DenseNet and Support Vector Machine classifications of major depressive disorder using vertex-wise cortical features

Major depressive disorder (MDD) is a complex psychiatric disorder that affects the lives of hundreds of millions of individuals around the globe. Even today, researchers debate if morphological alterations in the brain are linked to MDD, likely due to the heterogeneity of this disorder. The application of deep learning tools to neuroimaging data, capable of capturing complex non-linear patterns, has the potential to provide diagnostic and predictive biomarkers for MDD. However, previous attempts to demarcate MDD patients and healthy controls (HC) based on segmented cortical features via linear machine learning approaches have reported low accuracies. In this study, we used globally representative data from the ENIGMA-MDD working group containing an extensive sample of people with MDD (N=2,772) and HC (N=4,240), which allows a comprehensive analysis with generalizable results. Based on the hypothesis that integration of vertex-wise cortical features can improve classification performance, we evaluated the classification of a DenseNet and a Support Vector Machine (SVM), with the expectation that the former would outperform the latter. As we analyzed a multi-site sample, we additionally applied the ComBat harmonization tool to remove potential nuisance effects of site. We found that both classifiers exhibited close to chance performance (balanced accuracy DenseNet: 51%; SVM: 53%), when estimated on unseen sites. Slightly higher classification performance (balanced accuracy DenseNet: 58%; SVM: 55%) was found when the cross-validation folds contained subjects from all sites, indicating site effect. In conclusion, the integration of vertex-wise morphometric features and the use of the non-linear classifier did not lead to the differentiability between MDD and HC. Our results support the notion that MDD classification on this combination of features and classifiers is unfeasible

ENIGMA-anxiety working group : Rationale for and organization of large-scale neuroimaging studies of anxiety disorders

Altres ajuts: Anxiety Disorders Research Network European College of Neuropsychopharmacology; Claude Leon Postdoctoral Fellowship; Deutsche Forschungsgemeinschaft (DFG, German Research Foundation, 44541416-TRR58); EU7th Frame Work Marie Curie Actions International Staff Exchange Scheme grant 'European and South African Research Network in Anxiety Disorders' (EUSARNAD); Geestkracht programme of the Netherlands Organization for Health Research and Development (ZonMw, 10-000-1002); Intramural Research Training Award (IRTA) program within the National Institute of Mental Health under the Intramural Research Program (NIMH-IRP, MH002781); National Institute of Mental Health under the Intramural Research Program (NIMH-IRP, ZIA-MH-002782); SA Medical Research Council; U.S. National Institutes of Health grants (P01 AG026572, P01 AG055367, P41 EB015922, R01 AG060610, R56 AG058854, RF1 AG051710, U54 EB020403).Anxiety disorders are highly prevalent and disabling but seem particularly tractable to investigation with translational neuroscience methodologies. Neuroimaging has informed our understanding of the neurobiology of anxiety disorders, but research has been limited by small sample sizes and low statistical power, as well as heterogenous imaging methodology. The ENIGMA-Anxiety Working Group has brought together researchers from around the world, in a harmonized and coordinated effort to address these challenges and generate more robust and reproducible findings. This paper elaborates on the concepts and methods informing the work of the working group to date, and describes the initial approach of the four subgroups studying generalized anxiety disorder, panic disorder, social anxiety disorder, and specific phobia. At present, the ENIGMA-Anxiety database contains information about more than 100 unique samples, from 16 countries and 59 institutes. Future directions include examining additional imaging modalities, integrating imaging and genetic data, and collaborating with other ENIGMA working groups. The ENIGMA consortium creates synergy at the intersection of global mental health and clinical neuroscience, and the ENIGMA-Anxiety Working Group extends the promise of this approach to neuroimaging research on anxiety disorders

Multi-messenger observations of a binary neutron star merger

On 2017 August 17 a binary neutron star coalescence candidate (later designated GW170817) with merger time 12:41:04 UTC was observed through gravitational waves by the Advanced LIGO and Advanced Virgo detectors. The Fermi Gamma-ray Burst Monitor independently detected a gamma-ray burst (GRB 170817A) with a time delay of ~1.7 s with respect to the merger time. From the gravitational-wave signal, the source was initially localized to a sky region of 31 deg2 at a luminosity distance of 40+8-8 Mpc and with component masses consistent with neutron stars. The component masses were later measured to be in the range 0.86 to 2.26 Mo. An extensive observing campaign was launched across the electromagnetic spectrum leading to the discovery of a bright optical transient (SSS17a, now with the IAU identification of AT 2017gfo) in NGC 4993 (at ~40 Mpc) less than 11 hours after the merger by the One- Meter, Two Hemisphere (1M2H) team using the 1 m Swope Telescope. The optical transient was independently detected by multiple teams within an hour. Subsequent observations targeted the object and its environment. Early ultraviolet observations revealed a blue transient that faded within 48 hours. Optical and infrared observations showed a redward evolution over ~10 days. Following early non-detections, X-ray and radio emission were discovered at the transient’s position ~9 and ~16 days, respectively, after the merger. Both the X-ray and radio emission likely arise from a physical process that is distinct from the one that generates the UV/optical/near-infrared emission. No ultra-high-energy gamma-rays and no neutrino candidates consistent with the source were found in follow-up searches. These observations support the hypothesis that GW170817 was produced by the merger of two neutron stars in NGC4993 followed by a short gamma-ray burst (GRB 170817A) and a kilonova/macronova powered by the radioactive decay of r-process nuclei synthesized in the ejecta